Development and evaluation of spatial point process models for epidermal nerve fibers

Olsbo, Viktor; Myllymäki, Mari; Waller, Lance A.; Särkkä, Aila

doi:10.1016/j.mbs.2013.03.001

Cited by 9 publications

(42 citation statements)

References 12 publications

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“…One can see in Olsbo et al , that the estimates of κ are close to 0, indicating that the end points of one tree do not seem to avoid the nearest other base point. An effect of this would be that the spanned angles in the simulated patterns are larger than those found in the data.…”

Section: Models For Enf Structurementioning

confidence: 98%

“…The branch lengths are modelled by a gamma distribution, which has the density

f_{B} (b; α, β) = \frac{β^{α}}{normalΓ (α)} b^{α - 1} \exp (- βb), b \in (0, \infty), α \in (0, \infty), β \in (0, \infty),

where α and β are the shape and rate parameters, respectively. More details of the model, parameter estimation etc, can be found in .…”

Section: Models For Enf Structurementioning

confidence: 99%

“…where and are the shape and rate parameters, respectively. More details of the model, parameter estimation etc, can be found in [5].…”

Section: Noc Modelmentioning

confidence: 99%

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Discovering early diabetic neuropathy from epidermal nerve fiber patterns

Andersson

Guttorp

Särkkä

2016

Statistics in Medicine

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Epidermal nerve fibre (ENF) density and morphology are used to study small fibre involvement in diabetic, HIV, chemotherapy induced and other neuropathies. ENF density and summed length of ENFs per epidermal surface area are reduced, and ENFs may appear more clustered within the epidermis in subjects with small fibre neuropathy than in healthy subjects. Therefore, it is important to understand the spatial structure of ENFs. In this paper, we compare the ENF patterns between healthy subjects and subjects suffering from mild diabetic neuropathy. The study is based on suction skin blister specimens from the right foot of 32 healthy subjects and eight subjects with mild diabetic neuropathy. We regard the ENF entry point (location where the trunks of a nerve enters the epidermis) and ENF end point (termination of the nerve fibres) patterns as realizations of spatial point processes, and develop tools that can be used in the analysis and modelling of ENF patterns. We use spatial summary statistics and shift plots and define a new tool, reactive territory, to study the spatial patterns and to compare the patterns of the two groups. We will also introduce a simple model for these data in order to understand the growth process of the nerve fibres. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Models For Enf Structurementioning

confidence: 98%

“…The branch lengths are modelled by a gamma distribution, which has the density

f_{B} (b; α, β) = \frac{β^{α}}{normalΓ (α)} b^{α - 1} \exp (- βb), b \in (0, \infty), α \in (0, \infty), β \in (0, \infty),

where α and β are the shape and rate parameters, respectively. More details of the model, parameter estimation etc, can be found in .…”

Section: Models For Enf Structurementioning

confidence: 99%

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Discovering early diabetic neuropathy from epidermal nerve fiber patterns

Andersson

Guttorp

Särkkä

2016

Statistics in Medicine

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“…Spatial point patterns are a common form of observation in plant ecology (Waagepetersen et al, 2016), epidemiology (Diggle et al, 2005), astrophysics (Stoica et al, 2007), seismology (Schoenberg, 2003), social science (Amburgey, 1986), medicine (Olsbo et al, 2013) and criminology (Mohler et al, 2011). Although understanding single, univariate spatial point patterns and their generating point processes is important, frequently we observe labelled point processes or, more precisely, multiple types of points.…”

Section: Introductionmentioning

confidence: 99%

Detecting Multivariate Interactions in Spatial Point Patterns with Gibbs Models and Variable Selection

Rajala

Murrell

Olhede

2018

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary We propose a method for detecting significant interactions in very large multivariate spatial point patterns. This methodology thus develops high dimensional data understanding in the point process setting. The method is based on modelling the patterns by using a flexible Gibbs point process model to characterize point‐to‐point interactions at different spatial scales directly. By using the Gibbs framework significant interactions can also be captured at small scales. Subsequently, the Gibbs point process is fitted by using a pseudolikelihood approximation, and we select significant interactions automatically by using the group lasso penalty with this likelihood approximation. Thus we estimate the multivariate interactions stably even in this setting. We demonstrate the feasibility of the method with a simulation study and show its power by applying it to a large and complex rainforest plant population data set of 83 species.

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“…Strauss (250, 0.6, 0.03) MatClust(200, 0.06, 1) Strauss(350, 0.4, 0.03 Figure 7 shows point patterns of entry and end points of epidermal nerve fibers (ENFs) that were previously analysed in Olsbo et al (2013). The ENFs are thin nerve fibers living in the outmost layer of the skin called epidermis.…”

Section: The Last Line Ofmentioning

confidence: 99%